{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-23T03:01:18.054861Z",
     "start_time": "2020-10-23T03:01:18.050152Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn import preprocessing\n",
    "import numpy as np\n",
    "\n",
    "a=np.array([[10,2.7,3.6],\n",
    "            [-100,5,-2],\n",
    "            [120,20,40]],dtype=np.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-23T02:59:57.632028Z",
     "start_time": "2020-10-23T02:59:57.617464Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  10. ,    2.7,    3.6],\n",
       "       [-100. ,    5. ,   -2. ],\n",
       "       [ 120. ,   20. ,   40. ]])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-23T03:01:20.735394Z",
     "start_time": "2020-10-23T03:01:20.725557Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/root/anaconda3/envs/mytorch/lib/python3.8/site-packages/sklearn/preprocessing/_data.py:174: UserWarning: Numerical issues were encountered when centering the data and might not be solved. Dataset may contain too large values. You may need to prescale your features.\n",
      "  warnings.warn(\"Numerical issues were encountered \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[ 0.       , -0.8517071, -0.5513802],\n",
       "       [-1.2247449, -0.5518714, -0.852133 ],\n",
       "       [ 1.2247449,  1.4035785,  1.4035132]], dtype=float32)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preprocessing.scale(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-23T03:04:04.965251Z",
     "start_time": "2020-10-23T03:04:04.563063Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/root/anaconda3/envs/mytorch/lib/python3.8/site-packages/sklearn/utils/deprecation.py:143: FutureWarning: The sklearn.datasets.samples_generator module is  deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.datasets. Anything that cannot be imported from sklearn.datasets is now part of the private API.\n",
      "  warnings.warn(message, FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.datasets.samples_generator import make_classification\n",
    "from sklearn.svm import SVC\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-23T03:11:02.465389Z",
     "start_time": "2020-10-23T03:11:02.457156Z"
    }
   },
   "outputs": [],
   "source": [
    "X,y=make_classification(n_samples=300,n_features=2,n_redundant=0,n_informative=2,\n",
    "                       random_state=22,n_clusters_per_class=1,scale=100)\n",
    "\n",
    "X=preprocessing.scale(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-23T03:11:03.043185Z",
     "start_time": "2020-10-23T03:11:02.904105Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X[:,0],X[:,1],c=y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-23T03:16:26.728554Z",
     "start_time": "2020-10-23T03:16:26.711379Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC()"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=.3)\n",
    "clf=SVC()\n",
    "clf.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-23T03:16:55.138157Z",
     "start_time": "2020-10-23T03:16:55.128816Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9333333333333333"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
